Sociability and social interaction on social networking websites
Bibliographic record
Abstract
Purpose Social websites have become a major medium for social interaction. From Facebook to MySpace to emergent sites like Twitter, social websites are increasing exponentially in user numbers and unique visits every day. How do these websites encourage sociability? What features or design practices enable users to socialize with other users? The purpose of this paper is to explore sociability on the social web and details how different social websites encourage their users to interact. Design/methodology/approach Four social websites (Facebook, MySpace, LinkedIn and Twitter) were examined from a user study perspective. After thoroughly participating on the websites, a series of observations were recorded from each experience. These experiences were then compared to understand the different approaches of each website. Findings Social websites use a number of different approaches to encourage sociability amongst their users. Facebook promotes privacy and representing “real world” networks in web environment, while MySpace promotes publicity and representing both real world and virtual networks in a web environment. Niche websites like LinkedIn and Twitter focus on more specific aspects of community and technology, respectively. Originality/value A comparison of different models of sociability does not yet exist. This study focuses specifically on what makes social websites “social.”
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".